381 lines
15 KiB
Python
381 lines
15 KiB
Python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Parakeet model."""
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import json
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import tempfile
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import unittest
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from pathlib import Path
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from transformers import is_datasets_available, is_torch_available
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from transformers.testing_utils import cleanup, require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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if is_datasets_available():
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from datasets import Audio, load_dataset
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if is_torch_available():
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import torch
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from transformers import (
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AutoProcessor,
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ParakeetCTCConfig,
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ParakeetEncoder,
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ParakeetEncoderConfig,
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ParakeetForCTC,
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)
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class ParakeetEncoderModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=1024,
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is_training=True,
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hidden_size=64,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=256,
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hidden_act="silu",
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dropout=0, # so gradient checkpointing doesn't fail
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conv_kernel_size=9,
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subsampling_factor=8,
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subsampling_conv_channels=32,
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use_bias=True,
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num_mel_bins=80,
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scale_input=True,
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):
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# testing suite parameters
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.num_mel_bins = num_mel_bins
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self.is_training = is_training
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# config parameters
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.dropout = dropout
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self.conv_kernel_size = conv_kernel_size
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self.subsampling_factor = subsampling_factor
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self.subsampling_conv_channels = subsampling_conv_channels
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self.use_bias = use_bias
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self.num_mel_bins = num_mel_bins
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self.scale_input = scale_input
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# Calculate output sequence length after subsampling
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self.output_seq_length = seq_length // subsampling_factor
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self.encoder_seq_length = self.output_seq_length
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self.key_length = self.output_seq_length
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def prepare_config_and_inputs(self):
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input_features = floats_tensor([self.batch_size, self.seq_length, self.num_mel_bins])
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = self.get_config()
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return config, input_features, attention_mask
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def get_config(self):
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return ParakeetEncoderConfig(
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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dropout=self.dropout,
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dropout_positions=self.dropout,
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layerdrop=self.dropout,
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activation_dropout=self.dropout,
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attention_dropout=self.dropout,
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conv_kernel_size=self.conv_kernel_size,
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subsampling_factor=self.subsampling_factor,
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subsampling_conv_channels=self.subsampling_conv_channels,
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use_bias=self.use_bias,
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num_mel_bins=self.num_mel_bins,
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scale_input=self.scale_input,
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)
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def create_and_check_model(self, config, input_features, attention_mask):
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model = ParakeetEncoder(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(input_features, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, config.hidden_size)
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)
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def prepare_config_and_inputs_for_common(self):
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config, input_features, attention_mask = self.prepare_config_and_inputs()
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inputs_dict = {
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"input_features": input_features,
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"attention_mask": attention_mask,
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}
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return config, inputs_dict
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def check_ctc_loss(self, config, input_values, *args):
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model = ParakeetForCTC(config=config)
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model.to(torch_device)
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# make sure that dropout is disabled
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model.eval()
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input_values = input_values[:3]
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attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
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input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
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max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
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labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
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# pad input
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for i in range(len(input_lengths)):
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input_values[i, input_lengths[i] :] = 0.0
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attention_mask[i, input_lengths[i] :] = 0
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model.config.ctc_loss_reduction = "sum"
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sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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model.config.ctc_loss_reduction = "mean"
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mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
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self.parent.assertTrue(isinstance(sum_loss, float))
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self.parent.assertTrue(isinstance(mean_loss, float))
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@require_torch
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class ParakeetEncoderModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (ParakeetEncoder,) if is_torch_available() else ()
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_torch_exportable = True
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def setUp(self):
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self.model_tester = ParakeetEncoderModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ParakeetEncoderConfig, has_text_modality=False)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skip(reason="ParakeetEncoder does not use inputs_embeds")
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def test_model_get_set_embeddings(self):
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pass
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class ParakeetForCTCModelTester:
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def __init__(self, parent, encoder_kwargs=None, is_training=True, vocab_size=128, pad_token_id=0):
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if encoder_kwargs is None:
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encoder_kwargs = {}
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self.parent = parent
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self.encoder_model_tester = ParakeetEncoderModelTester(parent, **encoder_kwargs)
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self.is_training = is_training
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self.batch_size = self.encoder_model_tester.batch_size
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self.output_seq_length = self.encoder_model_tester.output_seq_length
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self.num_hidden_layers = self.encoder_model_tester.num_hidden_layers
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self.seq_length = vocab_size
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self.hidden_size = self.encoder_model_tester.hidden_size
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self.vocab_size = vocab_size
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self.pad_token_id = pad_token_id
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def prepare_config_and_inputs(self):
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_, input_features, attention_mask = self.encoder_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_features, attention_mask
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def get_config(self):
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return ParakeetCTCConfig.from_encoder_config(
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encoder_config=self.encoder_model_tester.get_config(),
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vocab_size=self.vocab_size,
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pad_token_id=self.pad_token_id,
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)
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def create_and_check_model(self, config, input_features, attention_mask):
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model = ParakeetForCTC(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(input_features, attention_mask=attention_mask)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.output_seq_length, self.vocab_size))
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def prepare_config_and_inputs_for_common(self):
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config, input_features, attention_mask = self.prepare_config_and_inputs()
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inputs_dict = {
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"input_features": input_features,
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"attention_mask": attention_mask,
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}
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return config, inputs_dict
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def test_ctc_loss_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.encoder_model_tester.check_ctc_loss(*config_and_inputs)
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@require_torch
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class ParakeetForCTCModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (ParakeetForCTC,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": ParakeetEncoder,
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"automatic-speech-recognition": ParakeetForCTC,
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}
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if is_torch_available()
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else {}
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)
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test_attention_outputs = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_torch_exportable = True
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_is_composite = True
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def setUp(self):
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self.model_tester = ParakeetForCTCModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ParakeetCTCConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skip(reason="ParakeetEncoder does not use inputs_embeds")
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def test_model_get_set_embeddings(self):
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pass
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# Original function assumes vision+text model, so overwrite since Parakeet is audio+text
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# Below is modified from `tests/models/granite_speech/test_modeling_granite_speech.py`
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def test_sdpa_can_dispatch_composite_models(self):
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if not self.has_attentions:
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self.skipTest(reason="Model architecture does not support attentions")
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if not self._is_composite:
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self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_sdpa = model_class.from_pretrained(tmpdirname)
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model_sdpa = model_sdpa.eval().to(torch_device)
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model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
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model_eager = model_eager.eval().to(torch_device)
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self.assertTrue(model_eager.config._attn_implementation == "eager")
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for name, submodule in model_eager.named_modules():
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class_name = submodule.__class__.__name__
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if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
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raise ValueError("The eager model should not have SDPA attention layers")
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@require_torch
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class ParakeetForCTCIntegrationTest(unittest.TestCase):
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_dataset = None
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@classmethod
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def setUp(cls):
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cls.checkpoint_name = "nvidia/parakeet-ctc-1.1b"
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cls.dtype = torch.bfloat16
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cls.processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
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def tearDown(self):
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cleanup(torch_device, gc_collect=True)
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@classmethod
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def _load_dataset(cls):
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# Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
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if cls._dataset is None:
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cls._dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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cls._dataset = cls._dataset.cast_column(
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"audio", Audio(sampling_rate=cls.processor.feature_extractor.sampling_rate)
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)
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def _load_datasamples(self, num_samples):
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self._load_dataset()
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ds = self._dataset
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speech_samples = ds.sort("id")[:num_samples]["audio"]
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return [x["array"] for x in speech_samples]
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@slow
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def test_1b_model_integration(self):
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"""
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bezzam reproducer (creates JSON directly in repo): https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_single-py
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eustlb reproducer: https://gist.github.com/eustlb/6e9e3aa85de3f7c340ec3c36e65f2fe6
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"""
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RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_single.json"
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with open(RESULTS_PATH, "r") as f:
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raw_data = json.load(f)
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EXPECTED_TOKEN_IDS = torch.tensor(raw_data["token_ids"])
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EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
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samples = self._load_datasamples(1)
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model = ParakeetForCTC.from_pretrained(self.checkpoint_name, torch_dtype=self.dtype, device_map=torch_device)
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model.eval()
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model.to(torch_device)
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# -- apply
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inputs = self.processor(samples)
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inputs.to(torch_device, dtype=self.dtype)
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predicted_ids = model.generate(**inputs)
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torch.testing.assert_close(predicted_ids.cpu(), EXPECTED_TOKEN_IDS)
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predicted_transcripts = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
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self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
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@slow
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def test_1b_model_integration_batched(self):
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"""
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bezzam reproducer (creates JSON directly in repo): https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_batched-py
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eustlb reproducer: https://gist.github.com/eustlb/575b5da58de34a70116a1955b1183596
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"""
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RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_batch.json"
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with open(RESULTS_PATH, "r") as f:
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raw_data = json.load(f)
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EXPECTED_TOKEN_IDS = torch.tensor(raw_data["token_ids"])
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EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
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samples = self._load_datasamples(5)
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model = ParakeetForCTC.from_pretrained(self.checkpoint_name, torch_dtype=self.dtype, device_map=torch_device)
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model.eval()
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model.to(torch_device)
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# -- apply
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inputs = self.processor(samples)
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inputs.to(torch_device, dtype=self.dtype)
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predicted_ids = model.generate(**inputs)
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torch.testing.assert_close(predicted_ids.cpu(), EXPECTED_TOKEN_IDS)
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predicted_transcripts = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
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self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
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